Latest AI and machine learning research in neurosurgery for healthcare professionals.
Abdominal aortic aneurysm (AAA) patients in the ICU represent a heterogeneous, high-risk population with mortality risk evolving across distinct clinical phases. Existing prognostic tools rely largely on Cox proportional hazards (Cox PH) nomograms with narrow predictor sets and single time horizons, leaving the value of modern machine learning, extended features, and external generalizability unch...
Cerebral aneurysms are localized dilations of intracranial arteries that may rupture and cause subarachnoid hemorrhage. Current assessment relies on human interpretation of imaging and clinical risk factors, but integrating vascular shape, flow-related information, and patient-level variables into a unified quantitative model remains challenging. This study develops a modular framework for cerebra...
The African clawed frog Xenopus laevis is a widely utilized model organism in biomedical research; however, significant challenges in experimental rep...
Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural ...
Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural ...
Orthogonal and Stiefel layers give neural weights exact spectral control, but they also impose a strong modeling constraint: all represented singular ...
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on bo...
We developed a standardized, reproducible preprocessing framework for computed tomography (CT) imaging data from multi-institutional repositories such...
Many dense prediction networks rely on additive feature transformations and model higher-order feature interactions only implicitly. Product units pro...
Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft sea...
Introduction: Precise anatomical navigation is fundamental to safe endoscopic pituitary surgery, a high-stakes procedure characterised by a challengin...
Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for i...
W4A4 quantization of large video diffusion Transformers offers substantial memory savings but is hindered by two main challenges: sparse large-magnitu...
The design of modern neural architectures has converged through incremental empirical choices, yet the mechanisms governing their training dynamics re...
Curvature evolution on a deforming surface is governed by the full change in the surface metric, but on biological surfaces captured by serial three-d...
Surgical training involves didactic teaching, mentor-led learning, surgical skills laboratories, and direct exposure to surgery; however, increasing c...
Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment,...
Soft-tissue deformation remains a major limitation in image-guided neurosurgery, where intra-operative anatomy can deviate substantially from pre-oper...
Hyperspectral imaging (HSI) allows researchers to study plant traits non-destructively. By capturing hundreds of narrow spectral bands per pixel, it r...
High inpatient opioid exposure is associated with increased risk of persistent opioid use. Early identification of high-risk patients may improve opio...